Overview

Dataset statistics

Number of variables42
Number of observations9840
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory336.0 B

Variable types

Categorical6
Text2
Numeric30
DateTime4

Alerts

country has constant value ""Constant
latitude is highly overall correlated with air_quality_Carbon_Monoxide and 5 other fieldsHigh correlation
longitude is highly overall correlated with regionHigh correlation
last_updated_epoch is highly overall correlated with uv_index and 2 other fieldsHigh correlation
temperature_celsius is highly overall correlated with temperature_fahrenheit and 3 other fieldsHigh correlation
temperature_fahrenheit is highly overall correlated with temperature_celsius and 3 other fieldsHigh correlation
wind_mph is highly overall correlated with wind_kph and 2 other fieldsHigh correlation
wind_kph is highly overall correlated with wind_mph and 2 other fieldsHigh correlation
wind_degree is highly overall correlated with wind_directionHigh correlation
pressure_mb is highly overall correlated with pressure_in and 1 other fieldsHigh correlation
pressure_in is highly overall correlated with pressure_mb and 1 other fieldsHigh correlation
precip_mm is highly overall correlated with precip_in and 1 other fieldsHigh correlation
precip_in is highly overall correlated with precip_mm and 1 other fieldsHigh correlation
humidity is highly overall correlated with temperature_celsius and 2 other fieldsHigh correlation
cloud is highly overall correlated with precip_mm and 2 other fieldsHigh correlation
feels_like_celsius is highly overall correlated with temperature_celsius and 2 other fieldsHigh correlation
feels_like_fahrenheit is highly overall correlated with temperature_celsius and 2 other fieldsHigh correlation
visibility_km is highly overall correlated with visibility_miles and 1 other fieldsHigh correlation
visibility_miles is highly overall correlated with visibility_km and 1 other fieldsHigh correlation
uv_index is highly overall correlated with last_updated_epochHigh correlation
gust_mph is highly overall correlated with wind_mph and 2 other fieldsHigh correlation
gust_kph is highly overall correlated with wind_mph and 2 other fieldsHigh correlation
air_quality_Carbon_Monoxide is highly overall correlated with latitude and 8 other fieldsHigh correlation
air_quality_Nitrogen_dioxide is highly overall correlated with air_quality_Carbon_Monoxide and 5 other fieldsHigh correlation
air_quality_Sulphur_dioxide is highly overall correlated with air_quality_Carbon_Monoxide and 5 other fieldsHigh correlation
air_quality_PM2.5 is highly overall correlated with latitude and 6 other fieldsHigh correlation
air_quality_PM10 is highly overall correlated with latitude and 6 other fieldsHigh correlation
air_quality_us-epa-index is highly overall correlated with latitude and 6 other fieldsHigh correlation
air_quality_gb-defra-index is highly overall correlated with latitude and 6 other fieldsHigh correlation
moon_illumination is highly overall correlated with last_updated_epoch and 1 other fieldsHigh correlation
region is highly overall correlated with latitude and 1 other fieldsHigh correlation
condition_text is highly overall correlated with visibility_km and 1 other fieldsHigh correlation
wind_direction is highly overall correlated with wind_degreeHigh correlation
moon_phase is highly overall correlated with last_updated_epoch and 1 other fieldsHigh correlation
timezone is highly imbalanced (96.6%)Imbalance
precip_mm has 6996 (71.1%) zerosZeros
precip_in has 7610 (77.3%) zerosZeros
cloud has 206 (2.1%) zerosZeros
visibility_km has 216 (2.2%) zerosZeros
visibility_miles has 235 (2.4%) zerosZeros
air_quality_Ozone has 266 (2.7%) zerosZeros

Reproduction

Analysis started2023-09-18 07:29:55.984074
Analysis finished2023-09-18 07:30:58.130294
Duration1 minute and 2.15 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

country
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
India
9840 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters49200
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndia
2nd rowIndia
3rd rowIndia
4th rowIndia
5th rowIndia

Common Values

ValueCountFrequency (%)
India 9840
100.0%

Length

2023-09-18T13:00:58.171227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-18T13:00:58.227819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
india 9840
100.0%

Most occurring characters

ValueCountFrequency (%)
I 9840
20.0%
n 9840
20.0%
d 9840
20.0%
i 9840
20.0%
a 9840
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39360
80.0%
Uppercase Letter 9840
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 9840
25.0%
d 9840
25.0%
i 9840
25.0%
a 9840
25.0%
Uppercase Letter
ValueCountFrequency (%)
I 9840
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49200
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 9840
20.0%
n 9840
20.0%
d 9840
20.0%
i 9840
20.0%
a 9840
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 9840
20.0%
n 9840
20.0%
d 9840
20.0%
i 9840
20.0%
a 9840
20.0%
Distinct543
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
2023-09-18T13:00:58.319331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length18
Median length14
Mean length7.4998984
Min length3

Characters and Unicode

Total characters73799
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAshoknagar
2nd rowRaisen
3rd rowChhindwara
4th rowBetul
5th rowHoshangabad
ValueCountFrequency (%)
godavari 46
 
0.5%
puri 37
 
0.4%
gaya 36
 
0.4%
south 36
 
0.4%
udaipur 32
 
0.3%
east 28
 
0.3%
barddhaman 27
 
0.3%
kolkata 26
 
0.3%
siang 26
 
0.3%
nagar 24
 
0.2%
Other values (548) 9876
96.9%
2023-09-18T13:00:58.508093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 15554
21.1%
r 6889
 
9.3%
i 4685
 
6.3%
h 4316
 
5.8%
n 4142
 
5.6%
u 4105
 
5.6%
l 2747
 
3.7%
d 2563
 
3.5%
g 2267
 
3.1%
o 2234
 
3.0%
Other values (41) 24297
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 63215
85.7%
Uppercase Letter 10212
 
13.8%
Space Separator 354
 
0.5%
Dash Punctuation 18
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 15554
24.6%
r 6889
10.9%
i 4685
 
7.4%
h 4316
 
6.8%
n 4142
 
6.6%
u 4105
 
6.5%
l 2747
 
4.3%
d 2563
 
4.1%
g 2267
 
3.6%
o 2234
 
3.5%
Other values (15) 13713
21.7%
Uppercase Letter
ValueCountFrequency (%)
B 1252
12.3%
S 1178
11.5%
K 1035
 
10.1%
M 778
 
7.6%
D 707
 
6.9%
G 569
 
5.6%
A 557
 
5.5%
J 540
 
5.3%
P 522
 
5.1%
N 457
 
4.5%
Other values (14) 2617
25.6%
Space Separator
ValueCountFrequency (%)
354
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73427
99.5%
Common 372
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 15554
21.2%
r 6889
 
9.4%
i 4685
 
6.4%
h 4316
 
5.9%
n 4142
 
5.6%
u 4105
 
5.6%
l 2747
 
3.7%
d 2563
 
3.5%
g 2267
 
3.1%
o 2234
 
3.0%
Other values (39) 23925
32.6%
Common
ValueCountFrequency (%)
354
95.2%
- 18
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 15554
21.1%
r 6889
 
9.3%
i 4685
 
6.3%
h 4316
 
5.8%
n 4142
 
5.6%
u 4105
 
5.6%
l 2747
 
3.7%
d 2563
 
3.5%
g 2267
 
3.1%
o 2234
 
3.0%
Other values (41) 24297
32.9%

region
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
Uttar Pradesh
1099 
Madhya Pradesh
864 
Rajasthan
661 
Andhra Pradesh
 
586
Maharashtra
 
579
Other values (28)
6051 

Length

Max length27
Median length16
Mean length10.111992
Min length3

Characters and Unicode

Total characters99502
Distinct characters42
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMadhya Pradesh
2nd rowMadhya Pradesh
3rd rowMadhya Pradesh
4th rowMadhya Pradesh
5th rowMadhya Pradesh

Common Values

ValueCountFrequency (%)
Uttar Pradesh 1099
 
11.2%
Madhya Pradesh 864
 
8.8%
Rajasthan 661
 
6.7%
Andhra Pradesh 586
 
6.0%
Maharashtra 579
 
5.9%
Bihar 522
 
5.3%
Tamil Nadu 504
 
5.1%
Orissa 434
 
4.4%
Karnataka 415
 
4.2%
Gujarat 414
 
4.2%
Other values (23) 3762
38.2%

Length

2023-09-18T13:00:58.588642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 2675
18.7%
uttar 1099
 
7.7%
madhya 864
 
6.0%
rajasthan 661
 
4.6%
andhra 586
 
4.1%
maharashtra 579
 
4.1%
bihar 522
 
3.7%
tamil 504
 
3.5%
nadu 504
 
3.5%
orissa 434
 
3.0%
Other values (33) 5866
41.0%

Most occurring characters

ValueCountFrequency (%)
a 21961
22.1%
r 9737
 
9.8%
h 9153
 
9.2%
s 6739
 
6.8%
t 5860
 
5.9%
d 5816
 
5.8%
4454
 
4.5%
n 4091
 
4.1%
e 3807
 
3.8%
P 3053
 
3.1%
Other values (32) 24831
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81185
81.6%
Uppercase Letter 13863
 
13.9%
Space Separator 4454
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 21961
27.1%
r 9737
12.0%
h 9153
11.3%
s 6739
 
8.3%
t 5860
 
7.2%
d 5816
 
7.2%
n 4091
 
5.0%
e 3807
 
4.7%
i 2756
 
3.4%
m 2229
 
2.7%
Other values (13) 9036
11.1%
Uppercase Letter
ValueCountFrequency (%)
P 3053
22.0%
M 1714
12.4%
U 1297
9.4%
K 1026
 
7.4%
A 1018
 
7.3%
B 899
 
6.5%
J 683
 
4.9%
R 661
 
4.8%
N 648
 
4.7%
T 544
 
3.9%
Other values (8) 2320
16.7%
Space Separator
ValueCountFrequency (%)
4454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 95048
95.5%
Common 4454
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 21961
23.1%
r 9737
10.2%
h 9153
9.6%
s 6739
 
7.1%
t 5860
 
6.2%
d 5816
 
6.1%
n 4091
 
4.3%
e 3807
 
4.0%
P 3053
 
3.2%
i 2756
 
2.9%
Other values (31) 22075
23.2%
Common
ValueCountFrequency (%)
4454
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 21961
22.1%
r 9737
 
9.8%
h 9153
 
9.2%
s 6739
 
6.8%
t 5860
 
5.9%
d 5816
 
5.8%
4454
 
4.5%
n 4091
 
4.1%
e 3807
 
3.8%
P 3053
 
3.1%
Other values (32) 24831
25.0%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct447
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.106256
Minimum8.08
Maximum34.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:00:58.654187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8.08
5-th percentile11.1
Q120.27
median23.97
Q326.7725
95-th percentile31.45
Maximum34.57
Range26.49
Interquartile range (IQR)6.5025

Descriptive statistics

Standard deviation5.7975992
Coefficient of variation (CV)0.25091037
Kurtosis-0.084382181
Mean23.106256
Median Absolute Deviation (MAD)3.24
Skewness-0.58145761
Sum227365.56
Variance33.612157
MonotonicityNot monotonic
2023-09-18T13:00:58.727917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.48 72
 
0.7%
25.67 72
 
0.7%
24.88 72
 
0.7%
23.83 72
 
0.7%
22.6 62
 
0.6%
22.3 54
 
0.5%
26.5 54
 
0.5%
10.77 54
 
0.5%
30.38 54
 
0.5%
26.22 54
 
0.5%
Other values (437) 9220
93.7%
ValueCountFrequency (%)
8.08 18
0.2%
8.51 18
0.2%
8.73 18
0.2%
8.88 18
0.2%
8.97 18
0.2%
9.17 18
0.2%
9.27 18
0.2%
9.38 18
0.2%
9.49 18
0.2%
9.58 18
0.2%
ValueCountFrequency (%)
34.57 18
0.2%
34.33 18
0.2%
34.23 18
0.2%
34.21 18
0.2%
34.17 18
0.2%
34.09 18
0.2%
34.03 18
0.2%
34.02 18
0.2%
33.88 18
0.2%
33.73 17
0.2%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct425
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.229436
Minimum68.97
Maximum95.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:00:58.800442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum68.97
5-th percentile72.93
Q176.07
median78.67
Q383.9
95-th percentile92.8
Maximum95.8
Range26.83
Interquartile range (IQR)7.83

Descriptive statistics

Standard deviation5.7611515
Coefficient of variation (CV)0.071808451
Kurtosis0.02755371
Mean80.229436
Median Absolute Deviation (MAD)3.26
Skewness0.83801438
Sum789457.65
Variance33.190867
MonotonicityNot monotonic
2023-09-18T13:00:58.873350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.77 72
 
0.7%
78.17 72
 
0.7%
75.02 54
 
0.5%
74.78 54
 
0.5%
88.27 54
 
0.5%
76.33 54
 
0.5%
73.88 54
 
0.5%
77.55 54
 
0.5%
79.83 54
 
0.5%
72.97 54
 
0.5%
Other values (415) 9264
94.1%
ValueCountFrequency (%)
68.97 18
0.2%
69.6 18
0.2%
70.07 18
0.2%
70.37 18
0.2%
70.47 18
0.2%
70.78 18
0.2%
70.83 18
0.2%
70.9 18
0.2%
70.98 18
0.2%
71.03 18
0.2%
ValueCountFrequency (%)
95.8 18
0.2%
95.37 18
0.2%
95.1 18
0.2%
94.9 18
0.2%
94.83 18
0.2%
94.58 18
0.2%
94.53 18
0.2%
94.52 18
0.2%
94.5 18
0.2%
94.37 18
0.2%

timezone
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
Asia/Kolkata
9786 
Asia/Dhaka
 
36
Asia/Karachi
 
18

Length

Max length12
Median length12
Mean length11.992683
Min length10

Characters and Unicode

Total characters118008
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia/Kolkata
2nd rowAsia/Kolkata
3rd rowAsia/Kolkata
4th rowAsia/Kolkata
5th rowAsia/Kolkata

Common Values

ValueCountFrequency (%)
Asia/Kolkata 9786
99.5%
Asia/Dhaka 36
 
0.4%
Asia/Karachi 18
 
0.2%

Length

2023-09-18T13:00:58.943449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-18T13:00:59.006026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
asia/kolkata 9786
99.5%
asia/dhaka 36
 
0.4%
asia/karachi 18
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 29520
25.0%
i 9858
 
8.4%
A 9840
 
8.3%
s 9840
 
8.3%
/ 9840
 
8.3%
k 9822
 
8.3%
K 9804
 
8.3%
o 9786
 
8.3%
l 9786
 
8.3%
t 9786
 
8.3%
Other values (4) 126
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88488
75.0%
Uppercase Letter 19680
 
16.7%
Other Punctuation 9840
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 29520
33.4%
i 9858
 
11.1%
s 9840
 
11.1%
k 9822
 
11.1%
o 9786
 
11.1%
l 9786
 
11.1%
t 9786
 
11.1%
h 54
 
0.1%
r 18
 
< 0.1%
c 18
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A 9840
50.0%
K 9804
49.8%
D 36
 
0.2%
Other Punctuation
ValueCountFrequency (%)
/ 9840
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 108168
91.7%
Common 9840
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 29520
27.3%
i 9858
 
9.1%
A 9840
 
9.1%
s 9840
 
9.1%
k 9822
 
9.1%
K 9804
 
9.1%
o 9786
 
9.0%
l 9786
 
9.0%
t 9786
 
9.0%
h 54
 
< 0.1%
Other values (3) 72
 
0.1%
Common
ValueCountFrequency (%)
/ 9840
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 29520
25.0%
i 9858
 
8.4%
A 9840
 
8.3%
s 9840
 
8.3%
/ 9840
 
8.3%
k 9822
 
8.3%
K 9804
 
8.3%
o 9786
 
8.3%
l 9786
 
8.3%
t 9786
 
8.3%
Other values (4) 126
 
0.1%

last_updated_epoch
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6940042 × 109
Minimum1.6932861 × 109
Maximum1.6947315 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:00:59.059030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.6932861 × 109
5-th percentile1.6932861 × 109
Q11.6936119 × 109
median1.6940412 × 109
Q31.6943868 × 109
95-th percentile1.6947306 × 109
Maximum1.6947315 × 109
Range1445400
Interquartile range (IQR)774900

Descriptive statistics

Standard deviation448779.84
Coefficient of variation (CV)0.00026492251
Kurtosis-1.2031219
Mean1.6940042 × 109
Median Absolute Deviation (MAD)428400
Skewness0.042380341
Sum1.6669001 × 1013
Variance2.0140334 × 1011
MonotonicityNot monotonic
2023-09-18T13:00:59.123343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1693525500 544
 
5.5%
1693286100 543
 
5.5%
1693698300 541
 
5.5%
1694473200 540
 
5.5%
1694701800 539
 
5.5%
1694127600 482
 
4.9%
1694558700 462
 
4.7%
1694300400 461
 
4.7%
1693784700 446
 
4.5%
1694214000 441
 
4.5%
Other values (24) 4841
49.2%
ValueCountFrequency (%)
1693286100 543
5.5%
1693365300 323
3.3%
1693366200 227
2.3%
1693439100 324
3.3%
1693440000 226
2.3%
1693525500 544
5.5%
1693611900 413
4.2%
1693612800 136
 
1.4%
1693698300 541
5.5%
1693699200 3
 
< 0.1%
ValueCountFrequency (%)
1694731500 208
 
2.1%
1694730600 342
3.5%
1694701800 539
5.5%
1694700900 5
 
0.1%
1694559600 86
 
0.9%
1694558700 462
4.7%
1694473200 540
5.5%
1694472300 6
 
0.1%
1694386800 433
4.4%
1694385900 112
 
1.1%
Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
Minimum2023-08-29 10:15:00
Maximum2023-09-15 04:30:00
2023-09-18T13:00:59.196874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:59.267659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temperature_celsius
Real number (ℝ)

HIGH CORRELATION 

Distinct302
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.225061
Minimum-2.6
Maximum38.3
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)0.1%
Memory size77.0 KiB
2023-09-18T13:00:59.345713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.6
5-th percentile19.3
Q123.6
median25.6
Q327.3
95-th percentile30.5
Maximum38.3
Range40.9
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation3.8382387
Coefficient of variation (CV)0.15215974
Kurtosis6.6421412
Mean25.225061
Median Absolute Deviation (MAD)1.9
Skewness-1.6097496
Sum248214.6
Variance14.732076
MonotonicityNot monotonic
2023-09-18T13:00:59.416276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 381
 
3.9%
26 336
 
3.4%
28 283
 
2.9%
25 282
 
2.9%
24 211
 
2.1%
29 180
 
1.8%
25.5 148
 
1.5%
24.9 138
 
1.4%
25.4 138
 
1.4%
25.6 134
 
1.4%
Other values (292) 7609
77.3%
ValueCountFrequency (%)
-2.6 1
< 0.1%
-2.4 1
< 0.1%
-1.8 1
< 0.1%
-1.6 1
< 0.1%
-0.9 1
< 0.1%
-0.6 1
< 0.1%
0.2 1
< 0.1%
0.8 2
< 0.1%
1.2 1
< 0.1%
1.3 1
< 0.1%
ValueCountFrequency (%)
38.3 1
 
< 0.1%
36.3 2
< 0.1%
35.6 1
 
< 0.1%
35.3 1
 
< 0.1%
35.2 1
 
< 0.1%
35.1 3
< 0.1%
35 1
 
< 0.1%
34.9 1
 
< 0.1%
34.7 4
< 0.1%
34.6 4
< 0.1%

temperature_fahrenheit
Real number (ℝ)

HIGH CORRELATION 

Distinct302
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.405234
Minimum27.3
Maximum100.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:00:59.490399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum27.3
5-th percentile66.7
Q174.5
median78.1
Q381.1
95-th percentile86.9
Maximum100.9
Range73.6
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation6.9092406
Coefficient of variation (CV)0.089260639
Kurtosis6.6393199
Mean77.405234
Median Absolute Deviation (MAD)3.4
Skewness-1.6092018
Sum761667.5
Variance47.737605
MonotonicityNot monotonic
2023-09-18T13:00:59.559628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.6 381
 
3.9%
78.8 336
 
3.4%
82.4 283
 
2.9%
77 282
 
2.9%
75.2 211
 
2.1%
84.2 180
 
1.8%
77.9 148
 
1.5%
76.8 138
 
1.4%
77.7 138
 
1.4%
78.1 134
 
1.4%
Other values (292) 7609
77.3%
ValueCountFrequency (%)
27.3 1
< 0.1%
27.7 1
< 0.1%
28.8 1
< 0.1%
29.1 1
< 0.1%
30.4 1
< 0.1%
30.9 1
< 0.1%
32.4 1
< 0.1%
33.4 2
< 0.1%
34.2 1
< 0.1%
34.3 1
< 0.1%
ValueCountFrequency (%)
100.9 1
 
< 0.1%
97.3 2
< 0.1%
96.1 1
 
< 0.1%
95.5 1
 
< 0.1%
95.4 1
 
< 0.1%
95.2 3
< 0.1%
95 1
 
< 0.1%
94.8 1
 
< 0.1%
94.5 4
< 0.1%
94.3 4
< 0.1%

condition_text
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
Clear
2423 
Partly cloudy
1866 
Mist
1395 
Light rain shower
949 
Patchy rain possible
943 
Other values (20)
2264 

Length

Max length35
Median length30
Mean length10.158638
Min length3

Characters and Unicode

Total characters99961
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPartly cloudy
2nd rowSunny
3rd rowPartly cloudy
4th rowCloudy
5th rowCloudy

Common Values

ValueCountFrequency (%)
Clear 2423
24.6%
Partly cloudy 1866
19.0%
Mist 1395
14.2%
Light rain shower 949
 
9.6%
Patchy rain possible 943
 
9.6%
Sunny 772
 
7.8%
Cloudy 414
 
4.2%
Moderate or heavy rain shower 244
 
2.5%
Overcast 239
 
2.4%
Fog 216
 
2.2%
Other values (15) 379
 
3.9%

Length

2023-09-18T13:00:59.633315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rain 2433
14.1%
clear 2423
14.1%
cloudy 2280
13.2%
partly 1866
10.8%
mist 1395
8.1%
shower 1223
7.1%
light 1132
6.6%
patchy 993
5.8%
possible 971
 
5.6%
sunny 772
 
4.5%
Other values (15) 1755
10.2%

Most occurring characters

ValueCountFrequency (%)
r 9076
 
9.1%
a 8717
 
8.7%
l 7668
 
7.7%
7403
 
7.4%
y 6256
 
6.3%
t 6220
 
6.2%
e 6125
 
6.1%
i 6099
 
6.1%
o 5419
 
5.4%
s 4864
 
4.9%
Other values (22) 32114
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82718
82.8%
Uppercase Letter 9840
 
9.8%
Space Separator 7403
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 9076
11.0%
a 8717
10.5%
l 7668
9.3%
y 6256
 
7.6%
t 6220
 
7.5%
e 6125
 
7.4%
i 6099
 
7.4%
o 5419
 
6.6%
s 4864
 
5.9%
n 4102
 
5.0%
Other values (12) 18172
22.0%
Uppercase Letter
ValueCountFrequency (%)
P 2859
29.1%
C 2837
28.8%
M 1758
17.9%
L 1084
 
11.0%
S 772
 
7.8%
O 239
 
2.4%
F 216
 
2.2%
T 56
 
0.6%
H 19
 
0.2%
Space Separator
ValueCountFrequency (%)
7403
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 92558
92.6%
Common 7403
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 9076
 
9.8%
a 8717
 
9.4%
l 7668
 
8.3%
y 6256
 
6.8%
t 6220
 
6.7%
e 6125
 
6.6%
i 6099
 
6.6%
o 5419
 
5.9%
s 4864
 
5.3%
n 4102
 
4.4%
Other values (21) 28012
30.3%
Common
ValueCountFrequency (%)
7403
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 9076
 
9.1%
a 8717
 
8.7%
l 7668
 
7.7%
7403
 
7.4%
y 6256
 
6.3%
t 6220
 
6.2%
e 6125
 
6.1%
i 6099
 
6.1%
o 5419
 
5.4%
s 4864
 
4.9%
Other values (22) 32114
32.1%

wind_mph
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1042988
Minimum2.2
Maximum25.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:00:59.699821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile2.2
Q13.4
median5.4
Q38.1
95-th percentile13
Maximum25.7
Range23.5
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation3.5560817
Coefficient of variation (CV)0.58255367
Kurtosis1.1395276
Mean6.1042988
Median Absolute Deviation (MAD)2.3
Skewness1.1317999
Sum60066.3
Variance12.645717
MonotonicityNot monotonic
2023-09-18T13:00:59.770435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 1395
 
14.2%
4.3 479
 
4.9%
3.8 463
 
4.7%
6.9 354
 
3.6%
5.6 345
 
3.5%
2.5 284
 
2.9%
4.5 263
 
2.7%
2.7 239
 
2.4%
3.1 236
 
2.4%
4.7 232
 
2.4%
Other values (81) 5550
56.4%
ValueCountFrequency (%)
2.2 1395
14.2%
2.5 284
 
2.9%
2.7 239
 
2.4%
2.9 225
 
2.3%
3.1 236
 
2.4%
3.4 226
 
2.3%
3.6 212
 
2.2%
3.8 463
 
4.7%
4 224
 
2.3%
4.3 479
 
4.9%
ValueCountFrequency (%)
25.7 1
 
< 0.1%
24.8 1
 
< 0.1%
24.6 1
 
< 0.1%
23.3 1
 
< 0.1%
22.8 1
 
< 0.1%
22.6 1
 
< 0.1%
22.1 1
 
< 0.1%
21.7 3
< 0.1%
21.3 5
0.1%
20.6 4
< 0.1%

wind_kph
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8266565
Minimum3.6
Maximum41.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:00:59.953218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile3.6
Q15.4
median8.6
Q313
95-th percentile20.9
Maximum41.4
Range37.8
Interquartile range (IQR)7.6

Descriptive statistics

Standard deviation5.7187218
Coefficient of variation (CV)0.58196008
Kurtosis1.1457102
Mean9.8266565
Median Absolute Deviation (MAD)3.6
Skewness1.1370164
Sum96694.3
Variance32.703779
MonotonicityNot monotonic
2023-09-18T13:01:00.024659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 1395
 
14.2%
6.8 479
 
4.9%
6.1 463
 
4.7%
11.2 354
 
3.6%
9 345
 
3.5%
4 284
 
2.9%
7.2 263
 
2.7%
4.3 239
 
2.4%
5 236
 
2.4%
7.6 232
 
2.4%
Other values (81) 5550
56.4%
ValueCountFrequency (%)
3.6 1395
14.2%
4 284
 
2.9%
4.3 239
 
2.4%
4.7 225
 
2.3%
5 236
 
2.4%
5.4 226
 
2.3%
5.8 212
 
2.2%
6.1 463
 
4.7%
6.5 224
 
2.3%
6.8 479
 
4.9%
ValueCountFrequency (%)
41.4 1
 
< 0.1%
40 1
 
< 0.1%
39.6 1
 
< 0.1%
37.4 1
 
< 0.1%
36.7 1
 
< 0.1%
36.4 1
 
< 0.1%
35.6 1
 
< 0.1%
34.9 3
< 0.1%
34.2 5
0.1%
33.1 4
< 0.1%

wind_degree
Real number (ℝ)

HIGH CORRELATION 

Distinct360
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.2185
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:00.102578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q1100
median233
Q3271.25
95-th percentile324
Maximum360
Range359
Interquartile range (IQR)171.25

Descriptive statistics

Standard deviation98.014747
Coefficient of variation (CV)0.50466227
Kurtosis-1.1882166
Mean194.2185
Median Absolute Deviation (MAD)67
Skewness-0.38689649
Sum1911110
Variance9606.8906
MonotonicityNot monotonic
2023-09-18T13:01:00.170391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
270 171
 
1.7%
10 158
 
1.6%
260 124
 
1.3%
250 118
 
1.2%
280 108
 
1.1%
240 102
 
1.0%
290 94
 
1.0%
300 85
 
0.9%
263 82
 
0.8%
262 78
 
0.8%
Other values (350) 8720
88.6%
ValueCountFrequency (%)
1 7
 
0.1%
2 10
 
0.1%
3 11
 
0.1%
4 7
 
0.1%
5 18
 
0.2%
6 4
 
< 0.1%
7 18
 
0.2%
8 10
 
0.1%
9 10
 
0.1%
10 158
1.6%
ValueCountFrequency (%)
360 12
0.1%
359 10
0.1%
358 6
 
0.1%
357 14
0.1%
356 11
0.1%
355 15
0.2%
354 11
0.1%
353 11
0.1%
352 8
0.1%
351 15
0.2%

wind_direction
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
W
1523 
WSW
1392 
WNW
916 
E
694 
ENE
647 
Other values (11)
4668 

Length

Max length3
Median length2
Mean length2.1833333
Min length1

Characters and Unicode

Total characters21484
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWNW
2nd rowWNW
3rd rowNW
4th rowWNW
5th rowW

Common Values

ValueCountFrequency (%)
W 1523
15.5%
WSW 1392
14.1%
WNW 916
9.3%
E 694
 
7.1%
ENE 647
 
6.6%
NE 569
 
5.8%
SW 556
 
5.7%
NW 524
 
5.3%
SE 517
 
5.3%
ESE 452
 
4.6%
Other values (6) 2050
20.8%

Length

2023-09-18T13:01:00.240551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w 1523
15.5%
wsw 1392
14.1%
wnw 916
9.3%
e 694
 
7.1%
ene 647
 
6.6%
ne 569
 
5.8%
sw 556
 
5.7%
nw 524
 
5.3%
se 517
 
5.3%
ese 452
 
4.6%
Other values (6) 2050
20.8%

Most occurring characters

ValueCountFrequency (%)
W 7879
36.7%
E 4650
21.6%
S 4576
21.3%
N 4379
20.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 21484
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 7879
36.7%
E 4650
21.6%
S 4576
21.3%
N 4379
20.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 21484
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 7879
36.7%
E 4650
21.6%
S 4576
21.3%
N 4379
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 7879
36.7%
E 4650
21.6%
S 4576
21.3%
N 4379
20.4%

pressure_mb
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1004.9433
Minimum997
Maximum1020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:00.301603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum997
5-th percentile1001
Q11002
median1005
Q31007
95-th percentile1010
Maximum1020
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1673575
Coefficient of variation (CV)0.0031517773
Kurtosis0.33042655
Mean1004.9433
Median Absolute Deviation (MAD)2
Skewness0.63020413
Sum9888642
Variance10.032153
MonotonicityNot monotonic
2023-09-18T13:01:00.357714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1002 1272
12.9%
1004 1237
12.6%
1003 1204
12.2%
1005 1060
10.8%
1006 949
9.6%
1007 779
7.9%
1001 776
7.9%
1008 740
7.5%
1009 577
5.9%
1010 333
 
3.4%
Other values (14) 913
9.3%
ValueCountFrequency (%)
997 4
 
< 0.1%
998 18
 
0.2%
999 88
 
0.9%
1000 320
 
3.3%
1001 776
7.9%
1002 1272
12.9%
1003 1204
12.2%
1004 1237
12.6%
1005 1060
10.8%
1006 949
9.6%
ValueCountFrequency (%)
1020 3
 
< 0.1%
1019 1
 
< 0.1%
1018 6
 
0.1%
1017 9
 
0.1%
1016 18
 
0.2%
1015 19
 
0.2%
1014 43
 
0.4%
1013 85
0.9%
1012 99
1.0%
1011 200
2.0%

pressure_in
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.674687
Minimum29.45
Maximum30.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:00.423333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum29.45
5-th percentile29.55
Q129.6
median29.66
Q329.74
95-th percentile29.84
Maximum30.12
Range0.67
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.093339548
Coefficient of variation (CV)0.0031454265
Kurtosis0.30540605
Mean29.674687
Median Absolute Deviation (MAD)0.07
Skewness0.63040407
Sum291998.92
Variance0.0087122712
MonotonicityNot monotonic
2023-09-18T13:01:00.490306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.59 606
 
6.2%
29.65 558
 
5.7%
29.62 545
 
5.5%
29.71 417
 
4.2%
29.68 411
 
4.2%
29.57 390
 
4.0%
29.67 387
 
3.9%
29.66 384
 
3.9%
29.6 362
 
3.7%
29.63 338
 
3.4%
Other values (54) 5442
55.3%
ValueCountFrequency (%)
29.45 4
 
< 0.1%
29.47 8
 
0.1%
29.48 10
 
0.1%
29.49 24
 
0.2%
29.5 34
 
0.3%
29.51 30
 
0.3%
29.52 73
0.7%
29.53 123
1.2%
29.54 167
1.7%
29.55 171
1.7%
ValueCountFrequency (%)
30.12 2
 
< 0.1%
30.11 1
 
< 0.1%
30.1 1
 
< 0.1%
30.06 4
< 0.1%
30.05 2
 
< 0.1%
30.04 6
0.1%
30.03 2
 
< 0.1%
30.02 1
 
< 0.1%
30.01 6
0.1%
30 3
< 0.1%

precip_mm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct118
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38617886
Minimum0
Maximum43.8
Zeros6996
Zeros (%)71.1%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:00.561913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile2
Maximum43.8
Range43.8
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation1.5957351
Coefficient of variation (CV)4.1321141
Kurtosis268.37829
Mean0.38617886
Median Absolute Deviation (MAD)0
Skewness13.059186
Sum3800
Variance2.5463706
MonotonicityNot monotonic
2023-09-18T13:01:00.627769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6996
71.1%
0.1 614
 
6.2%
0.2 324
 
3.3%
0.3 214
 
2.2%
0.4 178
 
1.8%
0.5 156
 
1.6%
0.6 138
 
1.4%
0.7 115
 
1.2%
0.8 80
 
0.8%
0.9 76
 
0.8%
Other values (108) 949
 
9.6%
ValueCountFrequency (%)
0 6996
71.1%
0.1 614
 
6.2%
0.2 324
 
3.3%
0.3 214
 
2.2%
0.4 178
 
1.8%
0.5 156
 
1.6%
0.6 138
 
1.4%
0.7 115
 
1.2%
0.8 80
 
0.8%
0.9 76
 
0.8%
ValueCountFrequency (%)
43.8 2
< 0.1%
42.9 1
< 0.1%
37.9 2
< 0.1%
26.2 1
< 0.1%
26.1 2
< 0.1%
24.8 1
< 0.1%
21.9 1
< 0.1%
19.4 1
< 0.1%
18.2 1
< 0.1%
16 1
< 0.1%

precip_in
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.015055894
Minimum0
Maximum1.72
Zeros7610
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:00.695779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.08
Maximum1.72
Range1.72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.062806576
Coefficient of variation (CV)4.1715607
Kurtosis267.49697
Mean0.015055894
Median Absolute Deviation (MAD)0
Skewness13.032641
Sum148.15
Variance0.003944666
MonotonicityNot monotonic
2023-09-18T13:01:00.764258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7610
77.3%
0.01 538
 
5.5%
0.02 472
 
4.8%
0.04 205
 
2.1%
0.03 195
 
2.0%
0.05 122
 
1.2%
0.07 94
 
1.0%
0.06 92
 
0.9%
0.14 68
 
0.7%
0.09 53
 
0.5%
Other values (52) 391
 
4.0%
ValueCountFrequency (%)
0 7610
77.3%
0.01 538
 
5.5%
0.02 472
 
4.8%
0.03 195
 
2.0%
0.04 205
 
2.1%
0.05 122
 
1.2%
0.06 92
 
0.9%
0.07 94
 
1.0%
0.08 42
 
0.4%
0.09 53
 
0.5%
ValueCountFrequency (%)
1.72 2
< 0.1%
1.69 1
 
< 0.1%
1.49 2
< 0.1%
1.03 3
< 0.1%
0.98 1
 
< 0.1%
0.86 1
 
< 0.1%
0.76 1
 
< 0.1%
0.72 1
 
< 0.1%
0.63 3
< 0.1%
0.61 1
 
< 0.1%

humidity
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.547459
Minimum19
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:00.844822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile54
Q171
median84
Q392
95-th percentile98
Maximum100
Range81
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.938768
Coefficient of variation (CV)0.17305037
Kurtosis0.020064574
Mean80.547459
Median Absolute Deviation (MAD)10
Skewness-0.78560257
Sum792587
Variance194.28924
MonotonicityNot monotonic
2023-09-18T13:01:00.928377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 669
 
6.8%
89 513
 
5.2%
84 385
 
3.9%
96 314
 
3.2%
88 294
 
3.0%
95 290
 
2.9%
90 272
 
2.8%
79 264
 
2.7%
92 260
 
2.6%
91 260
 
2.6%
Other values (67) 6319
64.2%
ValueCountFrequency (%)
19 1
 
< 0.1%
22 1
 
< 0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
29 3
< 0.1%
31 3
< 0.1%
32 3
< 0.1%
33 4
< 0.1%
ValueCountFrequency (%)
100 189
 
1.9%
99 161
 
1.6%
98 210
 
2.1%
97 259
 
2.6%
96 314
3.2%
95 290
2.9%
94 669
6.8%
93 260
 
2.6%
92 260
 
2.6%
91 260
 
2.6%

cloud
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.835874
Minimum0
Maximum100
Zeros206
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:01.014888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median50
Q375
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)59

Descriptive statistics

Standard deviation32.15251
Coefficient of variation (CV)0.67214221
Kurtosis-1.3486123
Mean47.835874
Median Absolute Deviation (MAD)29
Skewness0.12640851
Sum470705
Variance1033.7839
MonotonicityNot monotonic
2023-09-18T13:01:01.088971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 924
 
9.4%
75 620
 
6.3%
50 530
 
5.4%
5 218
 
2.2%
25 215
 
2.2%
0 206
 
2.1%
6 198
 
2.0%
8 177
 
1.8%
10 177
 
1.8%
4 175
 
1.8%
Other values (91) 6400
65.0%
ValueCountFrequency (%)
0 206
2.1%
1 5
 
0.1%
2 42
 
0.4%
3 122
1.2%
4 175
1.8%
5 218
2.2%
6 198
2.0%
7 147
1.5%
8 177
1.8%
9 169
1.7%
ValueCountFrequency (%)
100 924
9.4%
99 20
 
0.2%
98 19
 
0.2%
97 13
 
0.1%
96 19
 
0.2%
95 23
 
0.2%
94 25
 
0.3%
93 18
 
0.2%
92 27
 
0.3%
91 24
 
0.2%

feels_like_celsius
Real number (ℝ)

HIGH CORRELATION 

Distinct332
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.68315
Minimum-3.4
Maximum50.5
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)0.1%
Memory size77.0 KiB
2023-09-18T13:01:01.157803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile19.3
Q125.9
median28
Q330.325
95-th percentile34.9
Maximum50.5
Range53.9
Interquartile range (IQR)4.425

Descriptive statistics

Standard deviation4.8914848
Coefficient of variation (CV)0.17669538
Kurtosis4.5784274
Mean27.68315
Median Absolute Deviation (MAD)2.2
Skewness-1.27106
Sum272402.2
Variance23.926623
MonotonicityNot monotonic
2023-09-18T13:01:01.232474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.6 139
 
1.4%
25.4 136
 
1.4%
27.4 135
 
1.4%
28.6 133
 
1.4%
27.1 132
 
1.3%
25.9 128
 
1.3%
26.8 126
 
1.3%
26.3 125
 
1.3%
29.1 125
 
1.3%
26.1 122
 
1.2%
Other values (322) 8539
86.8%
ValueCountFrequency (%)
-3.4 1
 
< 0.1%
-2.6 1
 
< 0.1%
-2.4 2
< 0.1%
-1.8 1
 
< 0.1%
-1.3 1
 
< 0.1%
0.2 1
 
< 0.1%
0.4 1
 
< 0.1%
0.6 1
 
< 0.1%
0.8 3
< 0.1%
0.9 1
 
< 0.1%
ValueCountFrequency (%)
50.5 1
 
< 0.1%
47 1
 
< 0.1%
44.7 1
 
< 0.1%
43.4 2
< 0.1%
43.2 1
 
< 0.1%
41.1 1
 
< 0.1%
40.9 3
< 0.1%
40.8 2
< 0.1%
40.7 1
 
< 0.1%
40.3 1
 
< 0.1%

feels_like_fahrenheit
Real number (ℝ)

HIGH CORRELATION 

Distinct438
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.828679
Minimum25.9
Maximum122.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:01.305877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25.9
5-th percentile66.7
Q178.575
median82.4
Q386.6
95-th percentile94.8
Maximum122.9
Range97
Interquartile range (IQR)8.025

Descriptive statistics

Standard deviation8.8042061
Coefficient of variation (CV)0.10759316
Kurtosis4.5785009
Mean81.828679
Median Absolute Deviation (MAD)4
Skewness-1.2716832
Sum805194.2
Variance77.514045
MonotonicityNot monotonic
2023-09-18T13:01:01.373564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.9 137
 
1.4%
77.7 131
 
1.3%
78.6 127
 
1.3%
81.3 127
 
1.3%
80.2 125
 
1.3%
80.8 124
 
1.3%
83.5 122
 
1.2%
79.3 121
 
1.2%
79 119
 
1.2%
84.4 118
 
1.2%
Other values (428) 8589
87.3%
ValueCountFrequency (%)
25.9 1
 
< 0.1%
27.3 1
 
< 0.1%
27.7 2
< 0.1%
28.8 1
 
< 0.1%
29.7 1
 
< 0.1%
32.4 1
 
< 0.1%
32.7 1
 
< 0.1%
33.1 1
 
< 0.1%
33.4 3
< 0.1%
33.6 1
 
< 0.1%
ValueCountFrequency (%)
122.9 1
< 0.1%
116.6 1
< 0.1%
112.5 1
< 0.1%
110.2 1
< 0.1%
110.1 1
< 0.1%
109.7 1
< 0.1%
105.9 1
< 0.1%
105.7 1
< 0.1%
105.5 2
< 0.1%
105.4 2
< 0.1%

visibility_km
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4281707
Minimum0
Maximum10
Zeros216
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:01.437814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.8850585
Coefficient of variation (CV)0.34231135
Kurtosis0.94792952
Mean8.4281707
Median Absolute Deviation (MAD)0
Skewness-1.5819704
Sum82933.2
Variance8.3235625
MonotonicityNot monotonic
2023-09-18T13:01:01.497782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 6987
71.0%
9 544
 
5.5%
2 415
 
4.2%
4 313
 
3.2%
3 304
 
3.1%
7 287
 
2.9%
0 216
 
2.2%
5 206
 
2.1%
6 132
 
1.3%
2.5 114
 
1.2%
Other values (14) 322
 
3.3%
ValueCountFrequency (%)
0 216
2.2%
1 3
 
< 0.1%
1.2 2
 
< 0.1%
1.5 2
 
< 0.1%
1.6 12
 
0.1%
1.8 14
 
0.1%
2 415
4.2%
2.1 12
 
0.1%
2.2 13
 
0.1%
2.4 6
 
0.1%
ValueCountFrequency (%)
10 6987
71.0%
9 544
 
5.5%
8 17
 
0.2%
7 287
 
2.9%
6 132
 
1.3%
5 206
 
2.1%
4.5 15
 
0.2%
4 313
 
3.2%
3.5 111
 
1.1%
3.2 64
 
0.7%

visibility_miles
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9535569
Minimum0
Maximum6
Zeros235
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:01.551292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median6
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8680907
Coefficient of variation (CV)0.37712107
Kurtosis0.59872667
Mean4.9535569
Median Absolute Deviation (MAD)0
Skewness-1.4972256
Sum48743
Variance3.4897627
MonotonicityNot monotonic
2023-09-18T13:01:01.599648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 6987
71.0%
1 993
 
10.1%
5 544
 
5.5%
2 439
 
4.5%
3 338
 
3.4%
4 304
 
3.1%
0 235
 
2.4%
ValueCountFrequency (%)
0 235
 
2.4%
1 993
 
10.1%
2 439
 
4.5%
3 338
 
3.4%
4 304
 
3.1%
5 544
 
5.5%
6 6987
71.0%
ValueCountFrequency (%)
6 6987
71.0%
5 544
 
5.5%
4 304
 
3.1%
3 338
 
3.4%
2 439
 
4.5%
1 993
 
10.1%
0 235
 
2.4%

uv_index
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.028252
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:01.654253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2462071
Coefficient of variation (CV)1.1074596
Kurtosis1.449493
Mean2.028252
Median Absolute Deviation (MAD)0
Skewness1.8081745
Sum19958
Variance5.0454463
MonotonicityNot monotonic
2023-09-18T13:01:01.703963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 8077
82.1%
7 747
 
7.6%
8 417
 
4.2%
6 373
 
3.8%
5 166
 
1.7%
4 43
 
0.4%
3 7
 
0.1%
9 5
 
0.1%
2 5
 
0.1%
ValueCountFrequency (%)
1 8077
82.1%
2 5
 
0.1%
3 7
 
0.1%
4 43
 
0.4%
5 166
 
1.7%
6 373
 
3.8%
7 747
 
7.6%
8 417
 
4.2%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 417
 
4.2%
7 747
 
7.6%
6 373
 
3.8%
5 166
 
1.7%
4 43
 
0.4%
3 7
 
0.1%
2 5
 
0.1%
1 8077
82.1%

gust_mph
Real number (ℝ)

HIGH CORRELATION 

Distinct147
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.060661
Minimum0
Maximum44.5
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:01.775196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q16
median9.4
Q313.4
95-th percentile19.9
Maximum44.5
Range44.5
Interquartile range (IQR)7.4

Descriptive statistics

Standard deviation5.4405415
Coefficient of variation (CV)0.54077379
Kurtosis0.6506704
Mean10.060661
Median Absolute Deviation (MAD)3.8
Skewness0.71780328
Sum98996.9
Variance29.599492
MonotonicityNot monotonic
2023-09-18T13:01:01.844849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.5 214
 
2.2%
8.9 202
 
2.1%
6.9 199
 
2.0%
10.7 195
 
2.0%
8.1 192
 
2.0%
5.6 186
 
1.9%
11.6 177
 
1.8%
6.5 177
 
1.8%
11.2 176
 
1.8%
6 173
 
1.8%
Other values (137) 7949
80.8%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.2 7
 
0.1%
0.4 25
 
0.3%
0.7 9
 
0.1%
0.9 41
0.4%
1.1 39
0.4%
1.3 54
0.5%
1.6 67
0.7%
1.8 75
0.8%
2 51
0.5%
ValueCountFrequency (%)
44.5 1
< 0.1%
43.2 1
< 0.1%
39.4 2
< 0.1%
38.5 1
< 0.1%
35.1 1
< 0.1%
34.7 1
< 0.1%
33.6 1
< 0.1%
32.7 1
< 0.1%
32 2
< 0.1%
31.8 2
< 0.1%

gust_kph
Real number (ℝ)

HIGH CORRELATION 

Distinct147
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.191199
Minimum0
Maximum71.6
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:01.916464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19.7
median15.1
Q321.6
95-th percentile32
Maximum71.6
Range71.6
Interquartile range (IQR)11.9

Descriptive statistics

Standard deviation8.7555438
Coefficient of variation (CV)0.54075944
Kurtosis0.64872241
Mean16.191199
Median Absolute Deviation (MAD)6.1
Skewness0.71718895
Sum159321.4
Variance76.659546
MonotonicityNot monotonic
2023-09-18T13:01:02.092528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.7 214
 
2.2%
14.4 202
 
2.1%
11.2 199
 
2.0%
17.3 195
 
2.0%
13 192
 
2.0%
9 186
 
1.9%
18.7 177
 
1.8%
10.4 177
 
1.8%
18 176
 
1.8%
9.7 173
 
1.8%
Other values (137) 7949
80.8%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.4 7
 
0.1%
0.7 25
 
0.3%
1.1 9
 
0.1%
1.4 41
0.4%
1.8 39
0.4%
2.2 54
0.5%
2.5 67
0.7%
2.9 75
0.8%
3.2 51
0.5%
ValueCountFrequency (%)
71.6 1
< 0.1%
69.5 1
< 0.1%
63.4 2
< 0.1%
61.9 1
< 0.1%
56.5 1
< 0.1%
55.8 1
< 0.1%
54 1
< 0.1%
52.6 1
< 0.1%
51.5 2
< 0.1%
51.1 2
< 0.1%

air_quality_Carbon_Monoxide
Real number (ℝ)

HIGH CORRELATION 

Distinct291
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean517.49069
Minimum113.5
Maximum5127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:02.165957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum113.5
5-th percentile195.3
Q1253.7
median387.2
Q3680.9
95-th percentile1174.9
Maximum5127
Range5013.5
Interquartile range (IQR)427.2

Descriptive statistics

Standard deviation378.28301
Coefficient of variation (CV)0.73099481
Kurtosis16.943795
Mean517.49069
Median Absolute Deviation (MAD)163.6
Skewness2.9095707
Sum5092108.4
Variance143098.03
MonotonicityNot monotonic
2023-09-18T13:01:02.237152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
253.7 128
 
1.3%
223.6 112
 
1.1%
250.3 111
 
1.1%
240.3 108
 
1.1%
243.7 107
 
1.1%
247 104
 
1.1%
217 103
 
1.0%
227 102
 
1.0%
220.3 100
 
1.0%
233.7 100
 
1.0%
Other values (281) 8765
89.1%
ValueCountFrequency (%)
113.5 2
 
< 0.1%
120.2 2
 
< 0.1%
125.2 3
< 0.1%
128.5 1
 
< 0.1%
130.2 5
0.1%
133.5 1
 
< 0.1%
136.9 1
 
< 0.1%
138.5 1
 
< 0.1%
140.2 1
 
< 0.1%
141.9 2
 
< 0.1%
ValueCountFrequency (%)
5127 1
 
< 0.1%
4753.1 2
< 0.1%
4432.7 1
 
< 0.1%
4379.3 1
 
< 0.1%
3952 1
 
< 0.1%
3898.6 1
 
< 0.1%
3791.8 1
 
< 0.1%
3685 2
< 0.1%
3578.2 1
 
< 0.1%
3311.2 4
< 0.1%

air_quality_Ozone
Real number (ℝ)

ZEROS 

Distinct348
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.963831
Minimum0
Maximum158.8
Zeros266
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:02.309890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q112.3
median24.3
Q340.1
95-th percentile73.7
Maximum158.8
Range158.8
Interquartile range (IQR)27.8

Descriptive statistics

Standard deviation23.135489
Coefficient of variation (CV)0.79877172
Kurtosis2.7025146
Mean28.963831
Median Absolute Deviation (MAD)13.4
Skewness1.3540391
Sum285004.1
Variance535.25086
MonotonicityNot monotonic
2023-09-18T13:01:02.383887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 266
 
2.7%
0.1 91
 
0.9%
24.7 89
 
0.9%
23.6 88
 
0.9%
23.3 87
 
0.9%
24 84
 
0.9%
29 83
 
0.8%
24.3 82
 
0.8%
27.5 77
 
0.8%
25.4 76
 
0.8%
Other values (338) 8817
89.6%
ValueCountFrequency (%)
0 266
2.7%
0.1 91
 
0.9%
0.2 49
 
0.5%
0.3 56
 
0.6%
0.4 32
 
0.3%
0.5 31
 
0.3%
0.6 40
 
0.4%
0.7 26
 
0.3%
0.8 22
 
0.2%
0.9 20
 
0.2%
ValueCountFrequency (%)
158.8 1
< 0.1%
155.9 1
< 0.1%
154.5 2
< 0.1%
153.1 2
< 0.1%
151.6 2
< 0.1%
147.3 1
< 0.1%
144.5 1
< 0.1%
141.6 1
< 0.1%
140.2 2
< 0.1%
138.8 1
< 0.1%

air_quality_Nitrogen_dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct272
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1537195
Minimum0
Maximum141.2
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:02.457395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q12.7
median5.3
Q310.6
95-th percentile24.7
Maximum141.2
Range141.2
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation8.7609179
Coefficient of variation (CV)1.0744689
Kurtosis17.691255
Mean8.1537195
Median Absolute Deviation (MAD)3.2
Skewness3.1188716
Sum80232.6
Variance76.753683
MonotonicityNot monotonic
2023-09-18T13:01:02.530996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 162
 
1.6%
4.2 156
 
1.6%
2.7 137
 
1.4%
2.4 135
 
1.4%
3.9 130
 
1.3%
2.3 128
 
1.3%
2 127
 
1.3%
3.6 120
 
1.2%
3.3 117
 
1.2%
2.1 114
 
1.2%
Other values (262) 8514
86.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.1 71
0.7%
0.2 90
0.9%
0.3 49
0.5%
0.4 56
0.6%
0.5 65
0.7%
0.6 77
0.8%
0.7 67
0.7%
0.8 74
0.8%
0.9 78
0.8%
ValueCountFrequency (%)
141.2 1
 
< 0.1%
115.2 1
 
< 0.1%
87.7 1
 
< 0.1%
85.7 1
 
< 0.1%
77.5 1
 
< 0.1%
75.4 2
< 0.1%
69.9 2
< 0.1%
69.2 1
 
< 0.1%
67.9 1
 
< 0.1%
67.2 3
< 0.1%

air_quality_Sulphur_dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct280
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0049695
Minimum0
Maximum541.7
Zeros69
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:02.613228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.9
median2.2
Q35.1
95-th percentile16.7
Maximum541.7
Range541.7
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation13.205666
Coefficient of variation (CV)2.6385107
Kurtosis521.81072
Mean5.0049695
Median Absolute Deviation (MAD)1.6
Skewness18.032482
Sum49248.9
Variance174.38961
MonotonicityNot monotonic
2023-09-18T13:01:02.692231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 358
 
3.6%
0.5 329
 
3.3%
0.6 306
 
3.1%
0.3 305
 
3.1%
0.2 270
 
2.7%
0.8 265
 
2.7%
0.7 263
 
2.7%
1.2 235
 
2.4%
0.9 232
 
2.4%
1 228
 
2.3%
Other values (270) 7049
71.6%
ValueCountFrequency (%)
0 69
 
0.7%
0.1 188
1.9%
0.2 270
2.7%
0.3 305
3.1%
0.4 358
3.6%
0.5 329
3.3%
0.6 306
3.1%
0.7 263
2.7%
0.8 265
2.7%
0.9 232
2.4%
ValueCountFrequency (%)
541.7 1
< 0.1%
423.4 1
< 0.1%
324.3 1
< 0.1%
316.6 1
< 0.1%
312.8 1
< 0.1%
270.8 1
< 0.1%
228.9 1
< 0.1%
198.4 1
< 0.1%
183.1 1
< 0.1%
169.8 1
< 0.1%

air_quality_PM2.5
Real number (ℝ)

HIGH CORRELATION 

Distinct1468
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.88438
Minimum0.5
Maximum410.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:02.770739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile2.1
Q19.4
median23.4
Q350.925
95-th percentile124.9
Maximum410.9
Range410.4
Interquartile range (IQR)41.525

Descriptive statistics

Standard deviation41.709817
Coefficient of variation (CV)1.1009766
Kurtosis6.9966314
Mean37.88438
Median Absolute Deviation (MAD)16.8
Skewness2.1958136
Sum372782.3
Variance1739.7089
MonotonicityNot monotonic
2023-09-18T13:01:02.844312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 50
 
0.5%
2.1 43
 
0.4%
12.8 41
 
0.4%
1.3 40
 
0.4%
1.2 40
 
0.4%
6.1 38
 
0.4%
6.3 38
 
0.4%
3.2 37
 
0.4%
4.5 37
 
0.4%
2.4 37
 
0.4%
Other values (1458) 9439
95.9%
ValueCountFrequency (%)
0.5 6
 
0.1%
0.6 6
 
0.1%
0.7 19
0.2%
0.8 20
0.2%
0.9 26
0.3%
1 25
0.3%
1.1 34
0.3%
1.2 40
0.4%
1.3 40
0.4%
1.4 35
0.4%
ValueCountFrequency (%)
410.9 2
< 0.1%
346.3 4
< 0.1%
342.2 1
 
< 0.1%
318.2 1
 
< 0.1%
317.8 1
 
< 0.1%
310.3 1
 
< 0.1%
301.1 1
 
< 0.1%
295.3 1
 
< 0.1%
293.8 1
 
< 0.1%
286.5 1
 
< 0.1%

air_quality_PM10
Real number (ℝ)

HIGH CORRELATION 

Distinct1657
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.369014
Minimum0.7
Maximum466.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:02.916886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile3
Q113.1
median31.2
Q363.4
95-th percentile149.705
Maximum466.7
Range466
Interquartile range (IQR)50.3

Descriptive statistics

Standard deviation49.579969
Coefficient of variation (CV)1.0466751
Kurtosis6.5332244
Mean47.369014
Median Absolute Deviation (MAD)21.8
Skewness2.1155298
Sum466111.1
Variance2458.1733
MonotonicityNot monotonic
2023-09-18T13:01:02.983889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.6 36
 
0.4%
2.1 36
 
0.4%
2 34
 
0.3%
2.2 33
 
0.3%
3.6 32
 
0.3%
4.3 32
 
0.3%
3.9 31
 
0.3%
6.2 31
 
0.3%
2.5 30
 
0.3%
4.1 30
 
0.3%
Other values (1647) 9515
96.7%
ValueCountFrequency (%)
0.7 2
 
< 0.1%
0.8 4
 
< 0.1%
0.9 5
 
0.1%
1 4
 
< 0.1%
1.1 7
 
0.1%
1.2 13
0.1%
1.3 14
0.1%
1.4 17
0.2%
1.5 18
0.2%
1.6 19
0.2%
ValueCountFrequency (%)
466.7 2
< 0.1%
463.1 1
 
< 0.1%
411.6 1
 
< 0.1%
379.9 1
 
< 0.1%
377.5 4
< 0.1%
363.5 1
 
< 0.1%
362.7 1
 
< 0.1%
349.3 2
< 0.1%
342.7 2
< 0.1%
330.1 1
 
< 0.1%

air_quality_us-epa-index
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1465447
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:03.041440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1728664
Coefficient of variation (CV)0.54639739
Kurtosis-0.55196242
Mean2.1465447
Median Absolute Deviation (MAD)1
Skewness0.72677312
Sum21122
Variance1.3756157
MonotonicityNot monotonic
2023-09-18T13:01:03.097502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 3794
38.6%
2 2867
29.1%
4 1515
 
15.4%
3 1403
 
14.3%
5 241
 
2.4%
6 20
 
0.2%
ValueCountFrequency (%)
1 3794
38.6%
2 2867
29.1%
3 1403
 
14.3%
4 1515
 
15.4%
5 241
 
2.4%
6 20
 
0.2%
ValueCountFrequency (%)
6 20
 
0.2%
5 241
 
2.4%
4 1515
 
15.4%
3 1403
 
14.3%
2 2867
29.1%
1 3794
38.6%

air_quality_gb-defra-index
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9007114
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:03.153765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.291601
Coefficient of variation (CV)0.84384633
Kurtosis-0.69418455
Mean3.9007114
Median Absolute Deviation (MAD)1
Skewness0.92269484
Sum38383
Variance10.834637
MonotonicityNot monotonic
2023-09-18T13:01:03.204478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 3099
31.5%
2 1889
19.2%
10 1558
15.8%
3 1265
12.9%
4 534
 
5.4%
5 425
 
4.3%
6 354
 
3.6%
8 258
 
2.6%
9 238
 
2.4%
7 220
 
2.2%
ValueCountFrequency (%)
1 3099
31.5%
2 1889
19.2%
3 1265
12.9%
4 534
 
5.4%
5 425
 
4.3%
6 354
 
3.6%
7 220
 
2.2%
8 258
 
2.6%
9 238
 
2.4%
10 1558
15.8%
ValueCountFrequency (%)
10 1558
15.8%
9 238
 
2.4%
8 258
 
2.6%
7 220
 
2.2%
6 354
 
3.6%
5 425
 
4.3%
4 534
 
5.4%
3 1265
12.9%
2 1889
19.2%
1 3099
31.5%
Distinct117
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
Minimum2023-09-18 04:44:00
Maximum2023-09-18 06:40:00
2023-09-18T13:01:03.267999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:01:03.341297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sunset
Date

Distinct121
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
Minimum2023-09-18 17:13:00
Maximum2023-09-18 19:14:00
2023-09-18T13:01:03.419205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:01:03.491311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct790
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:03.619633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.167378
Min length8

Characters and Unicode

Total characters80367
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st row05:42 PM
2nd row05:39 PM
3rd row05:32 PM
4th row05:36 PM
5th row05:38 PM
ValueCountFrequency (%)
pm 5880
29.9%
am 3411
 
17.3%
no 549
 
2.8%
moonrise 549
 
2.8%
05:20 54
 
0.3%
05:18 50
 
0.3%
05:23 44
 
0.2%
05:17 41
 
0.2%
05:14 41
 
0.2%
05:12 35
 
0.2%
Other values (708) 9026
45.9%
2023-09-18T13:01:03.811895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10545
13.1%
9840
12.2%
: 9291
11.6%
M 9291
11.6%
P 5880
 
7.3%
1 5655
 
7.0%
2 3880
 
4.8%
5 3806
 
4.7%
A 3411
 
4.2%
3 3018
 
3.8%
Other values (13) 15750
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37164
46.2%
Uppercase Letter 19131
23.8%
Space Separator 9840
 
12.2%
Other Punctuation 9291
 
11.6%
Lowercase Letter 4941
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10545
28.4%
1 5655
15.2%
2 3880
 
10.4%
5 3806
 
10.2%
3 3018
 
8.1%
4 2862
 
7.7%
6 2066
 
5.6%
7 1783
 
4.8%
9 1775
 
4.8%
8 1774
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
o 1647
33.3%
e 549
 
11.1%
s 549
 
11.1%
i 549
 
11.1%
r 549
 
11.1%
n 549
 
11.1%
m 549
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
M 9291
48.6%
P 5880
30.7%
A 3411
 
17.8%
N 549
 
2.9%
Space Separator
ValueCountFrequency (%)
9840
100.0%
Other Punctuation
ValueCountFrequency (%)
: 9291
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56295
70.0%
Latin 24072
30.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10545
18.7%
9840
17.5%
: 9291
16.5%
1 5655
10.0%
2 3880
 
6.9%
5 3806
 
6.8%
3 3018
 
5.4%
4 2862
 
5.1%
6 2066
 
3.7%
7 1783
 
3.2%
Other values (2) 3549
 
6.3%
Latin
ValueCountFrequency (%)
M 9291
38.6%
P 5880
24.4%
A 3411
 
14.2%
o 1647
 
6.8%
e 549
 
2.3%
s 549
 
2.3%
i 549
 
2.3%
r 549
 
2.3%
n 549
 
2.3%
m 549
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80367
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10545
13.1%
9840
12.2%
: 9291
11.6%
M 9291
11.6%
P 5880
 
7.3%
1 5655
 
7.0%
2 3880
 
4.8%
5 3806
 
4.7%
A 3411
 
4.2%
3 3018
 
3.8%
Other values (13) 15750
19.6%
Distinct979
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
Minimum2023-09-18 02:15:00
Maximum2023-09-18 18:48:00
2023-09-18T13:01:03.893573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:01:03.963141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

moon_phase
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.0 KiB
Waning Crescent
3283 
Waning Gibbous
3275 
Full Moon
1100 
New Moon
1094 
Last Quarter
545 

Length

Max length15
Median length14
Mean length12.99685
Min length8

Characters and Unicode

Total characters127889
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWaxing Gibbous
2nd rowWaxing Gibbous
3rd rowWaxing Gibbous
4th rowWaxing Gibbous
5th rowWaxing Gibbous

Common Values

ValueCountFrequency (%)
Waning Crescent 3283
33.4%
Waning Gibbous 3275
33.3%
Full Moon 1100
 
11.2%
New Moon 1094
 
11.1%
Last Quarter 545
 
5.5%
Waxing Gibbous 543
 
5.5%

Length

2023-09-18T13:01:04.030389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-18T13:01:04.100908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
waning 6558
33.3%
gibbous 3818
19.4%
crescent 3283
16.7%
moon 2194
 
11.1%
full 1100
 
5.6%
new 1094
 
5.6%
last 545
 
2.8%
quarter 545
 
2.8%
waxing 543
 
2.8%

Most occurring characters

ValueCountFrequency (%)
n 19136
15.0%
i 10919
 
8.5%
9840
 
7.7%
o 8206
 
6.4%
e 8205
 
6.4%
a 8191
 
6.4%
s 7646
 
6.0%
b 7636
 
6.0%
W 7101
 
5.6%
g 7101
 
5.6%
Other values (14) 33908
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98369
76.9%
Uppercase Letter 19680
 
15.4%
Space Separator 9840
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 19136
19.5%
i 10919
11.1%
o 8206
8.3%
e 8205
8.3%
a 8191
8.3%
s 7646
 
7.8%
b 7636
 
7.8%
g 7101
 
7.2%
u 5463
 
5.6%
r 4373
 
4.4%
Other values (5) 11493
11.7%
Uppercase Letter
ValueCountFrequency (%)
W 7101
36.1%
G 3818
19.4%
C 3283
16.7%
M 2194
 
11.1%
F 1100
 
5.6%
N 1094
 
5.6%
L 545
 
2.8%
Q 545
 
2.8%
Space Separator
ValueCountFrequency (%)
9840
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118049
92.3%
Common 9840
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 19136
16.2%
i 10919
 
9.2%
o 8206
 
7.0%
e 8205
 
7.0%
a 8191
 
6.9%
s 7646
 
6.5%
b 7636
 
6.5%
W 7101
 
6.0%
g 7101
 
6.0%
u 5463
 
4.6%
Other values (13) 28445
24.1%
Common
ValueCountFrequency (%)
9840
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127889
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 19136
15.0%
i 10919
 
8.5%
9840
 
7.7%
o 8206
 
6.4%
e 8205
 
6.4%
a 8191
 
6.4%
s 7646
 
6.0%
b 7636
 
6.0%
W 7101
 
5.6%
g 7101
 
5.6%
Other values (14) 33908
26.5%

moon_illumination
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.936179
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.0 KiB
2023-09-18T13:01:04.162027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q122
median60
Q394
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)72

Descriptive statistics

Standard deviation36.604392
Coefficient of variation (CV)0.63180542
Kurtosis-1.4908601
Mean57.936179
Median Absolute Deviation (MAD)38
Skewness-0.28754581
Sum570092
Variance1339.8815
MonotonicityNot monotonic
2023-09-18T13:01:04.219348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
98 1100
 
11.2%
1 1094
 
11.1%
39 550
 
5.6%
99 549
 
5.6%
79 549
 
5.6%
49 549
 
5.6%
8 548
 
5.6%
14 546
 
5.5%
70 545
 
5.5%
60 545
 
5.5%
Other values (6) 3265
33.2%
ValueCountFrequency (%)
1 1094
11.1%
8 548
5.6%
14 546
5.5%
22 545
5.5%
30 545
5.5%
39 550
5.6%
49 549
5.6%
60 545
5.5%
70 545
5.5%
79 549
5.6%
ValueCountFrequency (%)
100 544
5.5%
99 549
5.6%
98 1100
11.2%
94 544
5.5%
93 543
5.5%
88 544
5.5%
79 549
5.6%
70 545
5.5%
60 545
5.5%
49 549
5.6%

Interactions

2023-09-18T13:00:55.722636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T12:59:58.339380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:00.249310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:02.264422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:04.469678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:06.791903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:08.743000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:10.615205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:12.684607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:14.856979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:16.708056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:18.787301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:20.596729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:22.632165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:24.571560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:26.562643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:28.428736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:30.340834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:32.207806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:34.172425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:36.105618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:38.137791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:40.153346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-18T13:00:44.404310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:46.477521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-18T13:00:55.782666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T12:59:58.407954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:00.310321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:02.334406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:04.542653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:06.861424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:08.799839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:10.683362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:12.763675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:14.917022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:16.772572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-18T13:00:28.486509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:30.400159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:32.267803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:34.235223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:36.163218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:38.222242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:40.212745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-18T13:00:06.929423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-18T13:00:10.756521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-18T13:00:50.413685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-18T13:00:55.969630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-18T13:00:19.034985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:20.858618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:22.903775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:24.833251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:26.820993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:28.675478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:30.615133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:32.461616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-18T13:00:42.585374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:44.701520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:46.730502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:48.574463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:50.480664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:52.286836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:54.178224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:56.031293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T12:59:58.677495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:00.572085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:02.638650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:04.849747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-09-18T13:00:06.424749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:08.441698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:10.305554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:12.356283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:14.548759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:16.406860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:18.479419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:20.316811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:22.327456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:24.262527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:26.251086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:28.134150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:30.051639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:31.919546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:33.875535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:35.800943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:37.773958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:39.852786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:41.966408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:44.094694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:46.174999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:48.032003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:49.940049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:51.753013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:53.647557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:55.435287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:57.384535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:00.007547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:01.910237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:04.198979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:06.497327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:08.500637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:10.360048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:12.416398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:14.610272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:16.468374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:18.544071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:20.371323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:22.386274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:24.324283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:26.313158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:28.190641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:30.107691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:31.976054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:33.936482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:35.863622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:37.853449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:39.911496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:42.035209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:44.152805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:46.235223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:48.089017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:49.995561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:51.809079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:53.704849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:55.493026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:57.442258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:00.067135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:02.076347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:04.268561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:06.564854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:08.558393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:10.420047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:12.476696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:14.670858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:16.528606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:18.604469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:20.428324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:22.447272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:24.386062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:26.381242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:28.247641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:30.166364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:32.032211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:33.993876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:35.922237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:37.918707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:39.970610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:42.100534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:44.215185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:46.295945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:48.146792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:50.051564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:51.863129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:53.759823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:55.549436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:57.500539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:00.128136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:02.141329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:04.336586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:06.650375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:08.621533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:10.487232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:12.541674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:14.734882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:16.590811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:18.667902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:20.483498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:22.510302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:24.450135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:26.443242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:28.308305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:30.224969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:32.093641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:34.055591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:35.983451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:37.991382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:40.030226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:42.171184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:44.278194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:46.355960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:48.207215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:50.109072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:51.923058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:53.818288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:55.607961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:57.559524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:00.188725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:02.201842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:04.403110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:06.724378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:08.682818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:10.556703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:12.605112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:14.796499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:16.646987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:18.725790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:20.538260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:22.570948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:24.510859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:26.502788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:28.369514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:30.282555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:32.149712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:34.113508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:36.043763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:38.063279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:40.092414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:42.235715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:44.340882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:46.417475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:48.266307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:50.168071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:51.978193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:53.873288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T13:00:55.664353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-18T13:01:04.459319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
latitudelongitudelast_updated_epochtemperature_celsiustemperature_fahrenheitwind_mphwind_kphwind_degreepressure_mbpressure_inprecip_mmprecip_inhumiditycloudfeels_like_celsiusfeels_like_fahrenheitvisibility_kmvisibility_milesuv_indexgust_mphgust_kphair_quality_Carbon_Monoxideair_quality_Ozoneair_quality_Nitrogen_dioxideair_quality_Sulphur_dioxideair_quality_PM2.5air_quality_PM10air_quality_us-epa-indexair_quality_gb-defra-indexmoon_illuminationregiontimezonecondition_textwind_directionmoon_phase
latitude1.000-0.000-0.0030.1080.108-0.282-0.282-0.378-0.262-0.263-0.155-0.141-0.258-0.3840.0690.0690.1900.1890.042-0.218-0.2180.5390.0870.2720.1270.5800.6130.5310.550-0.0000.7350.1580.1920.1890.000
longitude-0.0001.0000.0040.1040.104-0.333-0.333-0.174-0.263-0.2640.1820.1650.3070.2910.2110.211-0.180-0.1780.239-0.325-0.3250.333-0.2570.1810.1100.1900.0880.1960.1930.0000.7110.2310.1730.1640.000
last_updated_epoch-0.0030.0041.000-0.261-0.2610.1220.122-0.253-0.407-0.4080.0280.0290.4610.289-0.157-0.157-0.077-0.074-0.5750.2170.2170.120-0.1650.1420.043-0.094-0.146-0.089-0.087-0.9510.0000.0000.2650.1500.816
temperature_celsius0.1080.104-0.2611.0001.0000.1140.1140.108-0.223-0.218-0.166-0.162-0.544-0.2840.9640.9640.0370.0290.4000.0760.0760.2440.1740.2290.3180.3270.3930.2980.3110.1910.3760.1680.2590.1110.244
temperature_fahrenheit0.1080.104-0.2611.0001.0000.1140.1140.108-0.223-0.218-0.166-0.162-0.544-0.2840.9640.9640.0370.0290.4000.0760.0760.2440.1740.2290.3180.3270.3930.2980.3110.1910.3760.1680.2590.1110.246
wind_mph-0.282-0.3330.1220.1140.1141.0001.0000.268-0.007-0.007-0.043-0.039-0.1350.0250.0900.0900.1620.162-0.0450.8940.894-0.3460.264-0.1680.048-0.320-0.269-0.312-0.313-0.1750.2640.0410.1230.1530.119
wind_kph-0.282-0.3330.1220.1140.1141.0001.0000.268-0.007-0.007-0.043-0.039-0.1350.0250.0900.0900.1620.162-0.0450.8940.894-0.3460.264-0.1680.048-0.320-0.269-0.312-0.313-0.1750.2650.0420.1230.1570.120
wind_degree-0.378-0.174-0.2530.1080.1080.2680.2681.0000.2300.231-0.112-0.102-0.261-0.1000.0340.0340.0950.0950.1430.1950.195-0.4060.184-0.269-0.108-0.256-0.205-0.246-0.2550.2230.2740.0590.1060.8780.163
pressure_mb-0.262-0.263-0.407-0.223-0.223-0.007-0.0070.2301.0000.995-0.107-0.107-0.220-0.128-0.296-0.296-0.008-0.0110.231-0.094-0.094-0.5010.111-0.352-0.297-0.403-0.353-0.361-0.3760.3840.3110.0890.2190.1320.224
pressure_in-0.263-0.264-0.408-0.218-0.218-0.007-0.0070.2310.9951.000-0.111-0.110-0.221-0.129-0.292-0.292-0.016-0.0190.231-0.093-0.093-0.5030.113-0.351-0.295-0.404-0.353-0.362-0.3770.3850.3180.1120.2410.1290.224
precip_mm-0.1550.1820.028-0.166-0.166-0.043-0.043-0.112-0.107-0.1111.0000.9160.4150.600-0.074-0.074-0.255-0.256-0.055-0.034-0.0340.008-0.121-0.039-0.046-0.147-0.216-0.139-0.143-0.0150.0840.0320.3930.0180.054
precip_in-0.1410.1650.029-0.162-0.162-0.039-0.039-0.102-0.107-0.1100.9161.0000.3980.514-0.077-0.077-0.255-0.257-0.058-0.022-0.0220.017-0.127-0.028-0.048-0.135-0.199-0.126-0.131-0.0060.0840.0210.3830.0180.054
humidity-0.2580.3070.461-0.544-0.544-0.135-0.135-0.261-0.220-0.2210.4150.3981.0000.665-0.375-0.375-0.440-0.436-0.320-0.078-0.0780.011-0.350-0.024-0.072-0.204-0.319-0.194-0.204-0.3990.2190.0560.2650.1040.261
cloud-0.3840.2910.289-0.284-0.2840.0250.025-0.100-0.128-0.1290.6000.5140.6651.000-0.153-0.153-0.407-0.405-0.121-0.004-0.004-0.091-0.201-0.082-0.045-0.296-0.398-0.274-0.281-0.2920.2210.0330.4800.0680.191
feels_like_celsius0.0690.211-0.1570.9640.9640.0900.0900.034-0.296-0.292-0.074-0.077-0.375-0.1531.0001.000-0.022-0.0290.3790.0530.0530.3000.1250.2580.3490.3380.3740.3080.3210.0890.3740.1570.2530.1070.192
feels_like_fahrenheit0.0690.211-0.1570.9640.9640.0900.0900.034-0.296-0.292-0.074-0.077-0.375-0.1531.0001.000-0.022-0.0290.3790.0530.0530.3000.1250.2580.3490.3380.3740.3080.3210.0890.3740.1580.2530.1080.192
visibility_km0.190-0.180-0.0770.0370.0370.1620.1620.095-0.008-0.016-0.255-0.255-0.440-0.407-0.022-0.0221.0000.9990.0340.1170.117-0.0110.170-0.067-0.0750.0920.1260.0820.0860.0700.2360.0440.6690.0970.114
visibility_miles0.189-0.178-0.0740.0290.0290.1620.1620.095-0.011-0.019-0.256-0.257-0.436-0.405-0.029-0.0290.9991.0000.0330.1160.116-0.0130.169-0.070-0.0790.0890.1220.0780.0830.0670.2680.0380.7490.0990.104
uv_index0.0420.239-0.5750.4000.400-0.045-0.0450.1430.2310.231-0.055-0.058-0.320-0.1210.3790.3790.0340.0331.000-0.211-0.211-0.0100.159-0.0970.0760.0810.0980.0860.0780.4190.2500.0210.2870.1010.346
gust_mph-0.218-0.3250.2170.0760.0760.8940.8940.195-0.094-0.093-0.034-0.022-0.078-0.0040.0530.0530.1170.116-0.2111.0001.000-0.2750.230-0.1130.050-0.259-0.210-0.258-0.256-0.2220.2840.0420.1390.1400.118
gust_kph-0.218-0.3250.2170.0760.0760.8940.8940.195-0.094-0.093-0.034-0.022-0.078-0.0040.0530.0530.1170.116-0.2111.0001.000-0.2750.230-0.1130.050-0.259-0.210-0.258-0.256-0.2220.2840.0430.1400.1400.117
air_quality_Carbon_Monoxide0.5390.3330.1200.2440.244-0.346-0.346-0.406-0.501-0.5030.0080.0170.011-0.0910.3000.300-0.011-0.013-0.010-0.275-0.2751.000-0.2710.7680.5020.8300.7540.8000.813-0.0850.2200.0100.0740.1500.081
air_quality_Ozone0.087-0.257-0.1650.1740.1740.2640.2640.1840.1110.113-0.121-0.127-0.350-0.2010.1250.1250.1700.1690.1590.2300.230-0.2711.000-0.4170.001-0.0430.004-0.055-0.0480.0900.2030.0830.1110.1030.193
air_quality_Nitrogen_dioxide0.2720.1810.1420.2290.229-0.168-0.168-0.269-0.352-0.351-0.039-0.028-0.024-0.0820.2580.258-0.067-0.070-0.097-0.113-0.1130.768-0.4171.0000.7200.6210.5830.6020.613-0.1050.1530.0000.0740.0980.074
air_quality_Sulphur_dioxide0.1270.1100.0430.3180.3180.0480.048-0.108-0.297-0.295-0.046-0.048-0.072-0.0450.3490.349-0.075-0.0790.0760.0500.0500.5020.0010.7201.0000.5340.5070.5100.519-0.0780.0820.0000.0390.0320.027
air_quality_PM2.50.5800.190-0.0940.3270.327-0.320-0.320-0.256-0.403-0.404-0.147-0.135-0.204-0.2960.3380.3380.0920.0890.081-0.259-0.2590.830-0.0430.6210.5341.0000.9720.9550.9770.1240.2270.0000.0980.1210.073
air_quality_PM100.6130.088-0.1460.3930.393-0.269-0.269-0.205-0.353-0.353-0.216-0.199-0.319-0.3980.3740.3740.1260.1220.098-0.210-0.2100.7540.0040.5830.5070.9721.0000.9230.9460.1740.2380.0220.1160.1050.080
air_quality_us-epa-index0.5310.196-0.0890.2980.298-0.312-0.312-0.246-0.361-0.362-0.139-0.126-0.194-0.2740.3080.3080.0820.0780.086-0.258-0.2580.800-0.0550.6020.5100.9550.9231.0000.9550.1160.3710.0280.1450.1820.074
air_quality_gb-defra-index0.5500.193-0.0870.3110.311-0.313-0.313-0.255-0.376-0.377-0.143-0.131-0.204-0.2810.3210.3210.0860.0830.078-0.256-0.2560.813-0.0480.6130.5190.9770.9460.9551.0000.1160.2890.0360.1090.1380.073
moon_illumination-0.0000.000-0.9510.1910.191-0.175-0.1750.2230.3840.385-0.015-0.006-0.399-0.2920.0890.0890.0700.0670.419-0.222-0.222-0.0850.090-0.105-0.0780.1240.1740.1160.1161.0000.0000.0000.2030.1420.749
region0.7350.7110.0000.3760.3760.2640.2650.2740.3110.3180.0840.0840.2190.2210.3740.3740.2360.2680.2500.2840.2840.2200.2030.1530.0820.2270.2380.3710.2890.0001.0000.3790.1760.2250.000
timezone0.1580.2310.0000.1680.1680.0410.0420.0590.0890.1120.0320.0210.0560.0330.1570.1580.0440.0380.0210.0420.0430.0100.0830.0000.0000.0000.0220.0280.0360.0000.3791.0000.1130.0820.000
condition_text0.1920.1730.2650.2590.2590.1230.1230.1060.2190.2410.3930.3830.2650.4800.2530.2530.6690.7490.2870.1390.1400.0740.1110.0740.0390.0980.1160.1450.1090.2030.1760.1131.0000.0920.281
wind_direction0.1890.1640.1500.1110.1110.1530.1570.8780.1320.1290.0180.0180.1040.0680.1070.1080.0970.0990.1010.1400.1400.1500.1030.0980.0320.1210.1050.1820.1380.1420.2250.0820.0921.0000.169
moon_phase0.0000.0000.8160.2440.2460.1190.1200.1630.2240.2240.0540.0540.2610.1910.1920.1920.1140.1040.3460.1180.1170.0810.1930.0740.0270.0730.0800.0740.0730.7490.0000.0000.2810.1691.000

Missing values

2023-09-18T13:00:57.684667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-18T13:00:57.983533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

countrylocation_nameregionlatitudelongitudetimezonelast_updated_epochlast_updatedtemperature_celsiustemperature_fahrenheitcondition_textwind_mphwind_kphwind_degreewind_directionpressure_mbpressure_inprecip_mmprecip_inhumiditycloudfeels_like_celsiusfeels_like_fahrenheitvisibility_kmvisibility_milesuv_indexgust_mphgust_kphair_quality_Carbon_Monoxideair_quality_Ozoneair_quality_Nitrogen_dioxideair_quality_Sulphur_dioxideair_quality_PM2.5air_quality_PM10air_quality_us-epa-indexair_quality_gb-defra-indexsunrisesunsetmoonrisemoonsetmoon_phasemoon_illumination
0IndiaAshoknagarMadhya Pradesh24.5777.72Asia/Kolkata16932861002023-08-29 10:4527.581.5Partly cloudy12.820.5281WNW1008.029.770.00.0672629.785.510.06.07.014.823.8243.745.81.73.112.618.51205:59 AM06:41 PM05:42 PM03:38 AMWaxing Gibbous93
1IndiaRaisenMadhya Pradesh23.3377.80Asia/Kolkata16932861002023-08-29 10:4527.581.5Sunny9.615.5287WNW1008.029.780.00.0701930.086.010.06.07.011.218.0240.338.32.12.610.714.21106:00 AM06:40 PM05:39 PM03:41 AMWaxing Gibbous93
2IndiaChhindwaraMadhya Pradesh22.0778.93Asia/Kolkata16932861002023-08-29 10:4526.379.3Partly cloudy11.418.4317NW1009.029.780.00.0705128.282.810.06.07.013.221.2220.357.20.61.716.820.72205:56 AM06:34 PM05:32 PM03:39 AMWaxing Gibbous93
3IndiaBetulMadhya Pradesh21.8677.93Asia/Kolkata16932861002023-08-29 10:4525.678.1Cloudy10.516.9297WNW1009.029.800.00.0766527.681.710.06.06.013.020.9200.325.01.21.14.96.61106:00 AM06:38 PM05:36 PM03:43 AMWaxing Gibbous93
4IndiaHoshangabadMadhya Pradesh22.7577.72Asia/Kolkata16932861002023-08-29 10:4527.281.0Cloudy10.116.2274W1009.029.790.00.0748229.985.810.06.06.011.618.7257.030.82.21.811.414.81106:01 AM06:39 PM05:38 PM03:42 AMWaxing Gibbous93
5IndiaSehoreMadhya Pradesh23.2077.08Asia/Kolkata16932861002023-08-29 10:4525.978.6Cloudy9.815.8287WNW1009.029.790.00.0776828.182.610.06.06.011.418.4223.626.13.13.37.910.51106:03 AM06:42 PM05:42 PM03:44 AMWaxing Gibbous93
6IndiaJabalpurMadhya Pradesh23.1779.95Asia/Kolkata16932861002023-08-29 10:4528.082.4Mist9.415.1280W1010.029.830.10.0705031.087.95.03.06.013.922.3263.756.51.21.817.123.12205:51 AM06:31 PM05:30 PM03:32 AMWaxing Gibbous93
7IndiaNarsimhapurMadhya Pradesh22.9579.20Asia/Kolkata16932861002023-08-29 10:4527.781.9Sunny11.017.6277W1008.029.770.00.0712130.586.910.06.07.012.520.2260.452.91.21.817.323.72205:55 AM06:34 PM05:33 PM03:36 AMWaxing Gibbous93
8IndiaPannaMadhya Pradesh24.7280.20Asia/Kolkata16932861002023-08-29 10:4530.086.0Sunny11.618.7288WNW1007.029.720.00.0602233.191.610.06.08.013.421.6243.779.41.15.427.435.52305:49 AM06:31 PM05:32 PM03:27 AMWaxing Gibbous93
9IndiaUjjainMadhya Pradesh23.1875.77Asia/Kolkata16932861002023-08-29 10:4526.078.8Mist11.919.1300WNW1012.029.880.00.0745027.882.15.03.06.014.122.7210.332.21.71.75.77.61106:08 AM06:48 PM05:47 PM03:49 AMWaxing Gibbous93
countrylocation_nameregionlatitudelongitudetimezonelast_updated_epochlast_updatedtemperature_celsiustemperature_fahrenheitcondition_textwind_mphwind_kphwind_degreewind_directionpressure_mbpressure_inprecip_mmprecip_inhumiditycloudfeels_like_celsiusfeels_like_fahrenheitvisibility_kmvisibility_milesuv_indexgust_mphgust_kphair_quality_Carbon_Monoxideair_quality_Ozoneair_quality_Nitrogen_dioxideair_quality_Sulphur_dioxideair_quality_PM2.5air_quality_PM10air_quality_us-epa-indexair_quality_gb-defra-indexsunrisesunsetmoonrisemoonsetmoon_phasemoon_illumination
9830IndiaKolkataWest Bengal22.5788.37Asia/Kolkata16947315002023-09-15 04:1526.078.8Moderate or heavy rain with thunder8.113.0100E1003.029.621.30.059410030.586.82.51.01.015.024.1921.30.138.455.850.555.93605:23 AM05:42 PM04:34 AM05:29 PMNew Moon1
9831IndiaShahdaraDelhi28.6777.32Asia/Kolkata16947315002023-09-15 04:1530.086.0Mist2.23.610N1004.029.650.00.00845034.293.63.01.01.08.513.71735.70.042.241.0100.6130.541006:05 AM06:27 PM05:13 AM06:18 PMNew Moon1
9832IndiaJhargramWest Bengal22.4586.98Asia/Kolkata16947315002023-09-15 04:1525.277.4Fog6.510.484E1002.029.580.00.009810028.984.00.00.01.010.516.91014.74.827.412.581.584.441005:29 AM05:47 PM04:40 AM05:35 PMNew Moon1
9833IndiaBarddhamanWest Bengal23.2487.87Asia/Kolkata16947315002023-09-15 04:1525.277.4Overcast9.815.888E1003.029.620.00.00979528.783.710.06.01.015.224.5574.111.612.25.124.227.82305:25 AM05:44 PM04:35 AM05:32 PMNew Moon1
9834IndiaNarayanpetAndhra Pradesh16.7377.50Asia/Kolkata16947315002023-09-15 04:1523.774.7Clear11.418.4270W1006.029.710.00.00751625.577.910.06.01.018.129.2253.717.54.70.83.76.61106:08 AM06:23 PM05:25 AM06:09 PMNew Moon1
9835IndiaNiwariUttar Pradesh28.8877.53Asia/Kolkata16947315002023-09-15 04:1530.086.0Mist2.23.610N1004.029.650.00.00845036.196.93.01.01.010.316.61335.10.028.516.594.3113.941006:04 AM06:27 PM05:12 AM06:18 PMNew Moon1
9836IndiaSaitualMizoram23.9792.58Asia/Kolkata16947315002023-09-15 04:1520.268.4Mist2.23.6109ESE1008.029.780.00.00976120.268.42.01.01.02.94.7270.40.23.30.311.211.91105:06 AM05:25 PM04:15 AM05:13 PMNew Moon1
9837IndiaRanipetTamil Nadu12.9379.33Asia/Kolkata16947315002023-09-15 04:1526.078.8Partly cloudy8.714.0260W1006.029.720.00.00713827.882.010.06.01.014.122.7413.914.313.03.05.78.31106:02 AM06:15 PM05:21 AM05:59 PMNew Moon1
9838IndiaTenkasiTamil Nadu8.9777.30Asia/Kolkata16947315002023-09-15 04:1523.874.8Clear8.714.0265W1010.029.820.00.00871925.978.610.06.01.011.017.6213.624.02.40.72.93.61106:11 AM06:22 PM05:33 AM06:05 PMNew Moon1
9839IndiaPendraMaharashtra21.9374.15Asia/Kolkata16947315002023-09-15 04:1521.871.2Mist5.89.4256WSW1005.029.670.00.00946621.871.22.01.01.010.717.3210.313.44.23.312.114.91206:20 AM06:38 PM05:34 AM06:26 PMNew Moon1